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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Data for the Arctic Embed walkthrough

This dataset coresponds to the walkthrough example for using the Arctic Embed training code in ArcticTraining. See that README for more details.

Example: Selective downloads via Git LFS

Since this dataset contains various intermediate files not necessary for training, it can be helpful to use the Git LFS backend of Hugging Face Datasets to pull select files.

# First, ensure you have installed git-lfs (see `https://git-lfs.com/` for documentation).
git lfs install

# Clone the full precomputed data repository without large files as our `data/` directory.
mv ./data/.gitignore ./data.gitignore
rmdir ./data
GIT_LFS_SKIP_SMUDGE=1 git clone https://hf.co/datasets/Snowflake/arctic-embed-ft-v1.git ./data
mv ./data.gitignore ./data/.gitignore

# Ensure we have all the files you need for training downloaded from LFS.
cd data/
git lfs pull --include="combined/pretokenized/example_dot95/,eval/"

# Optional: Download more large files (e.g. everything but the very large precomputed embeddings).
git lfs pull --exclude="*embeddings*"
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